Conflict-monitoring theory in overtime: Is temporal learning a viable explanation for the congruency sequence effect?
Bibliographic record
Abstract
In interference tasks (e.g., Stroop, 1935), congruency effects are larger following a congruent versus an incongruent trial. This "congruency sequence effect" has been traditionally explained in terms of a conflict-monitoring mechanism that focuses attention toward relevant information when conflict has recently been experienced. More recently, it has been suggested that effects of this sort result from differences in the temporal expectancies formed following congruent trials (fast responding) versus incongruent trials (slow responding). Evidence supporting this "temporal-learning" account was recently reported for a similar effect, the finding that congruency effects are larger in a mostly congruent list than in a mostly incongruent list. That is, consistent with the idea that this "proportion-congruent effect" is based on different temporal expectancies following congruent versus incongruent trials in interference tasks, the proportion-congruent effect was eliminated on normal (i.e., immediate-response) trials when temporal expectancies were equated by requiring a delayed response on the prior trial. In two experiments, we examined whether this delayed-response procedure would have a similar impact on the congruency sequence effect. Consistent with the temporal-learning account (but not inconsistent with conflict-monitoring accounts), the congruency sequence effect on immediate-response trials was eliminated when the previous trial required a delayed response. However, no evidence supporting the temporal-learning account emerged from reanalyses of experiments requiring only immediate responses in which the response latency in the previous trial functioned as the temporal-expectancy index. Overall, the present results and analyses do not provide much evidence favoring the temporal-learning account over conflict-monitoring accounts of the congruency sequence effect. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".